Every night, massive ground-based telescopes like Gemini and Keck capture millions of photons from distant stars, galaxies, and fleeting cosmic events. But raw astronomical images are riddled with instrumental signatures, atmospheric distortions, and calibration challenges that can bury the very phenomena astronomers seek to understand. Enter POTPyRI - the Pipeline for Optical/infrared Telescopes in Python for Reducing Images.
This comprehensive data reduction pipeline transforms raw FITS files into science-ready observations through a sophisticated workflow. It automatically handles pixel calibration to remove detector artifacts, performs precise astrometry using astrometry.net and Gaia DR3 to map exact celestial coordinates, executes intelligent image stacking for enhanced signal-to-noise, and conducts both aperture and PSF photometry with proper flux calibration. Supporting major instruments like GMOS, LRIS, and BINOSPEC, POTPyRI streamlines what traditionally required weeks of manual processing into an automated pipeline.
Developed by the CIERA Transients team, this tool is particularly valuable for time-domain astronomy - tracking supernovae, variable stars, and other transient phenomena where rapid, reliable data reduction can mean the difference between catching a cosmic event and missing it forever. Whether you’re a graduate student analyzing your first observing run or a survey team processing thousands of images nightly, POTPyRI handles the technical complexity so you can focus on the science.
⭐ Stars: 7
💻 Language: Python
🔗 Repository: CIERA-Transients/POTPyRI